1. Field of the Invention
The present invention generally relates to the field of computer image processing of computer tomography (CT) data and, more particularly, to improving the quality of computed tomography images produced by low-powered CT scanners.
2. Background Description
U.S. Pat. No. 5,416,815, which is incorporated here by reference in its entirety, describes computer tomography (CT) systems and the method of image reconstruction from projections.
X-ray CT scanners are often employed in sub-optimal operating environments. For example, CT scanners are becoming more widely employed at sites other than hospitals. Such sites include small rural clinics, military battlefields, sites of outdoor sporting events, and archaeological sites where excavated artifacts are scanned. Such environments are considered sub-optimal for CT scanning for a variety of reasons, such as extremes in humidity and temperature, and lack of reliable power supplies.
Power supply problems are often dealt with by the use of battery-supplied power or generators. In both cases, however, limitations on power dictate that the CT scanner must operate with much lower power supply than is conventionally used for CT scanning. Currently, a typical low-powered cT scanner has generator power of about 6 W compared with 18 W for a standard clinical scanner, and operates with approximately one third of the X-ray tube current of a standard clinical CT scanner, e.g., 40 mA versus 120 mA. (See, for example, "Tomoscan EG&M Product Information Manual", Philips Medical Systems, 1996.) Since image quality is directly related to tube current, low-powered CT scanners produce lower quality CT images; that is, CT images with significantly more noise and less detail.
Two approaches to improving the quality of such images are (1) modify the CT scanner itself so that it produces good quality images when running on low power and (2) apply noise-reduction algorithms to the CT image. In the prior art, approach (1) is used in the "Tomoscan M" CT scanner that is manufactured by Philips Medical Systems (see, for example, the World Wide Web page "http://www/medical.philips.com/products/ct/tomo.sub.-- m/tomo.sub.-- m.htm). Prior art methods that use approach (2) fall into two groups. In the first group are the methods which are applied directly to the noisy images. See, for example, D. D. Robertson, P. J. Weiss, E. K. Fishman, D. Maghid, and P. S. Walker, "Evaluation of CT techniques for reducing artifacts in the presence of metallic orthopedic implants", Journal of Computer Assisted Tomography, Mar.-Apr. 1988, 12(2), pp. 236-41; Hamid Soltanian-Zedeh, Joe P. Windham, and Jalal Soltanianzadeh, "CT Artifact Correction: An Image Processing Approach", SPIE Medical Imaging '96, Newport Beach, Calif., Feb. 1996; and Heang K. Tuy, "An Algorithm to Reduce Clip Artifacts in CT Images", SPIE Vol. 1652 Medical Imaging VI: Image Processing (1992). Such methods (i.e., which apply image processing directly) process the corrupted CT image data only. These methods do not make use of the fact that essential image information has been completely erased by the image noise. This information cannot be recovered solely from the corrupted images themselves. Therefore, these methods are unable to recover this information.
With the second group of algorithmic methods, the projection data are processed directly and the images reconstructed from these modified projections. See, for example, G. H. Glover and n. J. Pelc, "An algorithm for the reduction of metal clip artifacts in CT reconstructions", Medical Physics, 8(6), Nov/Dec 1981, pp. 799-807; T. Hinderling, P. Ruegsegger, M. Anliker, and C. Dietschi, "Computed Tomography reconstruction from hollow projections: an application to in vivo evaluation of artificial hip joints", Journal of Computer Assisted Tomography, Feb. 1979, 3(1), pp. 52-57; W. A. Kalender, R. Hebel, and J. Ebersberger, "Reduction of CT artifacts caused by metallic implants", Radiology, Aug. 1987, 164(2), pp. 576-7; E. Klotz, W. A. Kalender, R. Sokiranski, and D. Felsenberg, "Algorithms for reduction of CT artifacts caused by metallic implants", Medical Imaging IV: PACS System Design and Evaluation, vol. 1234, Newport Beach, Calif., Feb. 1990, pp. 642-650; R. M. Lewitt and R. H. T. Bates, "Image reconstruction from projections: VI: Projection completion methods (computational examples)", Optik 50, 1978, pp. 269-278; B. E. Oppenheim, "Reconstruction tomography from incomplete projections", Reconstruction Tomography in Diagnostic and Nuclear Medicine, Ter-Pogossian (editor), University Park Press, Baltimore, 1977, pp. 155-183; and G. Wang, D. L. Snyder, A. O'Sullivan, and M. W. Vannier, "Iterative deblurring for CT metal artifact reduction", IEEE Trans. Medical Imaging, Oct. 1996, 14(5), pp. 657-664. such methods (i.e., which process projection data directly) process the projection data only. The algorithms in this second group of methods do not make use of the (noisy) CT image data. Further, these methods work only with a very specific type of projection data; that is, projection data that (i) have been highly-sampled, and (ii) are of high resolution. The methods will fail if applied to sparsely-sampled or low-resolution projection data.